Advances in technology and analytics have prompted a shift towards decentralized, distributed computing environments. The growth of data volume has created a need for economies of scale in computing. Thus, distributed environments and resource sharing have recently become increasingly popular. In a distributed environment, such as the cloud, consumer applications, such as email, document hosting, backup services, and banking and financial services, are deployed over a network to geographically distributed physical resources, such as a plurality of servers. Benefits to consumers include scalability, sustainability, and reliability. However, in addition to ensuring that consumer applications are allocated and reallocated optimally to the distributed resources to meet demands, distributed environment operators must often also consider how the interplay between the network layer and the geographically distributed physical layer can affect performance of particular consumer applications. For example, algorithmic or high-speed trading applications rely not only on computational speed, but also network latency, and consumers in this field often prefer that the servers on which their algorithms execute are geographically close to markets.
Distributed resources are now increasingly equipped with hardware and software that enable the resources to record and report operational information. With such operational information, distributed environment operators can analyze resource conditions and performance across a distributed environment through visualization techniques. In managing a distributed environment, it can be beneficial to collect resource information at certain intervals of time and display a graphical representation of one or more metrics across a given time period. Often, however, such time-series data are represented in the form of a line graph or plot, and are not easily manipulated by the user seeking to extract certain information quickly. Thus, line graphs and plots can often be ineffective in displaying certain metrics associated with the distributed resources.
There remains a need for techniques to collect, analyze, and visualize operational information in a distributed environment, and to allow for the configuring and provisioning of distributed resources based on monitored and measured operational data associated with the distributed resources in the distributed environment.
The disclosed subject matter is directed to methods and systems for collecting, processing, and visualizing operational data of distributed resources by a monitoring system in a distributed environment. The monitoring system herein described can be configured to generate a time-series graphical display of the operational data collected from the distributed resources in a distributed environment in the form of a heatmap.